Inject Docs

Inject Docs

Cross-Platform AI Agent Skill This skill works with any AI agent platform that supports the skills.sh standard.

Framework Documentation Injector

Inject compressed framework-specific best practices and documentation into the current project's CLAUDE.md or AGENTS.md file. This gives AI coding agents passive access to framework knowledge without requiring tool calls or skills.

Supported Frameworks

Anti-Hallucination Guidelines

CRITICAL:

1. Auto-detect the framework before running anything - check project files to identify the framework
2. Do NOT assume tools are available - verify Node.js/Python tooling exists based on framework
3. Do NOT claim success until verifying the target file exists and contains actual content
4. Read actual output - report what the commands say, not what is expected

Implementation Workflow

Phase 0: Framework Detection & Validation (REQUIRED)

Before running anything, auto-detect the framework and verify prerequisites:

1. Detect the framework:

• Check for package.json with next dependency → Next.js project
• Check for pyproject.toml with fastapi dependency → FastAPI project
• Check for requirements.txt containing fastapi → FastAPI project
• If multiple frameworks detected, prioritize based on arguments or ask user
• If no framework detected, STOP and inform the user: "Could not detect a supported framework (Next.js or FastAPI)."

2. Detect framework version (if applicable):

• For Next.js: extract version from package.json
• For FastAPI: extract version from pyproject.toml or requirements.txt
• Report the detected version to the user

3. Detect target file:

• Check if CLAUDE.md exists in the project root - use CLAUDE.md
• Else check if AGENTS.md exists - use AGENTS.md
• If neither exists, default to CLAUDE.md (Claude Code's native format)
• Inform the user which file will be updated

Phase 1: Run Framework-Specific Injection

Option A: Next.js Projects

Execute the Vercel codemod with the --output flag:

npx @next/codemod@canary agents-md --output <TARGET_FILE>
**What this does**:
- Auto-detects the Next.js version from package.json
- Downloads version-matching documentation from Vercel's servers
- Injects a compressed pipe-delimited index into the target file
- Downloads full docs to `.next-docs/` and adds it to `.gitignore`
- Non-interactive mode (no prompts)

**Important**:
- Requires network access
- Non-destructive: updates existing file without overwriting content
- Compresses ~40KB of docs into ~8KB (Vercel's agent evals showed 100% pass rate vs 53% baseline)

#### Option B: FastAPI Projects

Run the bundled injection script:

```bash
uv run "$(dirname "$0")/scripts/inject_fastapi_docs.py"

The script:

• Detects whether CLAUDE.md or AGENTS.md exists and targets the right file
• Checks if a "FastAPI Best Practices" section already exists (updates it if so, appends if not)
• Injects compressed best practices covering: domain-driven structure, async patterns, Pydantic validation, dependency injection, SQLAlchemy integration, error handling, testing, and Ruff code quality

Template for FastAPI injection (see references/fastapi-best-practices.md for full content):

## FastAPI Best Practices

### Project Structure
- Use domain-driven organization (by feature), not file-type organization
- Each domain is self-contained: router, schemas, models, service, dependencies
- Structure per domain:
 - `router.py` - API endpoints
 - `schemas.py` - Pydantic request/response models
 - `models.py` - Database models (SQLAlchemy)
 - `service.py` - Business logic
 - `dependencies.py` - Route-level dependencies
 - `constants.py`, `config.py`, `exceptions.py`, `utils.py`

### Async Patterns
- Use `async def` for non-blocking I/O (database queries, HTTP calls)
- Use `def` for blocking operations (FastAPI handles threadpool automatically)
- **NEVER** use `time.sleep` in async functions (blocks event loop)
- Use `await asyncio.sleep` for delays
- CPU-intensive work requires multiprocessing/Celery (not threads due to GIL)
- Prefer async database drivers (SQLAlchemy 2.0+ with asyncio)

### Import Discipline
- Use explicit imports with module names: `from src.auth import constants as auth_constants`
- Avoids hidden coupling and improves maintainability
- Critical when importing services or dependencies from other packages

### Validation & Dependencies
- Leverage Pydantic's built-in validation (regex, enums, email, URL, constraints)
- Create custom BaseModel for application-wide consistency
- Use dependencies for business logic validation (DB constraints, authorization, token parsing)
- Dependencies cache within request scope - chain them to avoid redundant computations

### Response Serialization
- Always use `response_model` parameter on endpoints
- Create custom encoders for special types (datetime, UUID)
- FastAPI auto-generates OpenAPI schemas from type hints

### Error Handling
- Define module-specific exception classes
- Raise from dependencies and service layer
- FastAPI auto-converts to HTTP responses
- Use HTTP status codes correctly (400 for client errors, 500 for server errors)

### Database Integration
- SQL-first design: design schema first, then models
- Enforce naming conventions at database level
- Use Alembic for migrations
- Prefer async drivers for scalability

### Testing
- Use async test clients from day one
- Configure fixtures for async operations
- Test at multiple levels: unit (service), integration (router), e2e

### Code Quality
- Use Ruff for linting and formatting (Python-focused, fast)
- Always include type hints for OpenAPI generation
- Enforce strict mypy or pyright type checking
- Use pre-commit hooks for quality gates

### REST Conventions
- Use correct HTTP methods: GET (read), POST (create), PUT/PATCH (update), DELETE (remove)
- Docstrings on endpoints for clarity in auto-generated docs
- Leverage FastAPI's OpenAPI `/docs` as primary API documentation

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